NNPDF

NNPDF是用於識別部分子分佈函數（）的首字母縮寫詞。 

NNPDF is the acronym used to identify the parton distribution functions from the NNPDF Collaboration. NNPDF parton densities are 
extracted from global fits to data based on a combination of a Monte Carlo method for uncertainty estimation and the use of 
neural networks as basic interpolating functions.

Methodology 

NNPDF途徑可以分為四個主要步驟：
 The generation of a large sample of Monte Carlo replicas of the original experimental data, in a way that central values, errors and correlations are reproduced with enough accuracy.
 The training (minimization of the ) of a set of PDFs parametrized by neural networks on each of the above MC replicas of the data. PDFs are parametrized at the initial evolution scale  and then evolved to the experimental data scale  by means of the DGLAP equations. Since the PDF parametrization is redundant, the minimization strategy is based in genetic algorithms as well as gradient descent based minimizers.
 The neural network training is stopped dynamically before entering into the overlearning regime, that is, so that the PDFs learn the physical laws which underlie experimental data without fitting simultaneously statistical noise.
 Once the training of the MC replicas has been completed, a set of statistical estimators can be applied to the set of PDFs, in order to assess the statistical consistency of the results. For example, the stability with respect PDF parametrization can be explicitly verified.

The set of  PDF sets (trained neural networks) provides a representation of the underlying PDF probability density, from which   any statistical estimator can be computed.

示例

版本 
NNPDF各版本：

所有PDF集都可通過LHAPDF界面和NNPDF網頁 獲得。

外部鏈接 
The NNPDF Collaboration 主頁 
Download NNPDF Parton Distribution sets
The NNPDF1.0 analysis 
The NNPDF Non-Singlet analysis 
NNPDF3.1 release 
NNPDF latest fitting code 
The LHAPDF interface